High speed memory access, and reduced power consumption are features that are demanded from semiconductor devices. In recent years, systems that have adopted multi-core processors for the execution of applications have resulted in faster access patterns to a memory device serving as a main memory (e.g., dynamic random access memory (DRAM)) and also more random access patterns. For example, a typical access pattern to the DRAM repeats bank activation, read access or write access, and bank precharge in the order. Access patterns to a memory device for faster access are needed. The efficiency and performance of a computing device may be affected by different memory device. Accordingly, a need exists for fast and efficient access patterns.
Tensors, which are generally geometric objects related to a linear system, may be utilized in machine learning and artificial intelligence applications. Tensor processing may include processing of matrix algebra or other linear systems analysis. Such processing may be intensive and repetitive, in that a common operand may be utilized several times, for example; in layered processing of tensors. Such repetition, combined with speed of processing, may necessitate repeated memory access to perform operations for tensor processing.